Langchain & Neo4j: Create Knowledge Graphs from Text 

Thanks for your support :)

thank you :)

yes, these tools and technologies are still improving everyday. Sometime not too far in the future, we will get minimum acceptable accuracy required..

thank you very much for your support :)

its better to define the schema explicitly for production usecases and some post processing of LLMs output for sanity checks. I've several videos on these concepts. I'll add more content on this topic...

thanks for your support :)

Langchain library is being updated very frequently. Pls check the latest docs/APIs. Did you resolve the issue ? Whats the error pls...

This should do ---- import os from langchain_community.graphs import Neo4jGraph os.environ["NEO4J_URI"] = "bolt://localhost:7687" os.environ["NEO4J_USERNAME"] = "neo4j" os.environ["NEO4J_PASSWORD"] = "password" graph = Neo4jGraph()

Structured data (sql db) is usually easy to query, if we can have a good text2LLM model, then we don't need any additional algorithmic techniques. Querying unstructured data is complex and hence we have RAG, knowledge graphs, and combination of them...

@@SridharKumarKannam what would be your recommendation for writing optimal few-shot query examples to further optimise the sql LLM agent and also to reduce the LLM tokens overlaid and context size with bloated info?

I don't have any structured course year on ML but I'm planning to do one.

It is resolved? What error are you getting? After installing neo4j desktop, you need to install a couple of plugins also from neo4j desktop.

@@SridharKumarKannam The error is no bolt://localhost:7687 URL not found or running, I want to connect my Neo4J desktop to my Python Script/Notebook and try connecting both. So that, I could make use of knowledge graphs to visualize information from certain given text.

you can use only one db in the free version.

Did you run the query multiple times and got the same output? All LLMs are stochastic (random) in nature, some times strange results are expected.

It will overwrite the DB. I'm sure Neo4J has capability to add new information, but I'm not sure if thats implemented yet in the langchain wrapper. If your database is not too large, you can re-create it, by adding the new information to raw data before extracting nodes/relationships...

thats right, Thanks for your support :)

The output format from LLM models is important for these things. stackoverflow.com/questions/78521181/llmgraphtransformer-convert-to-graph-documentsdocuments-attributeerror-str

I would think in the next few months, definitely within 6 months. There is a lot of work going on in converting free text to graphs.

@@SridharKumarKannam nice thank you for sharing your knowledge. One of the challenges with graphRAG seems that it requires allot more tokens / time (from testing it took about 40-60 seconds for 1 answer compared to 1-4 seconds for regular RAG, but the answer was 40% better) Do you think these challenges can be solved with the rise of LPU's/inference speed increasement ?

The ouput format of the llms can be the issue. Pls test using openai with a small text, if its working, then the issue is HF llm op format, choose a different model.

I've not tested that, what error are you getting?

@@SridharKumarKannam Thanks for your response! ----> 9 llm_transformer = LLMGraphTransformer(llm=llm) 215 schema = create_simple_model(allowed_nodes, allowed_relationships) --> 216 structured_llm = llm.with_structured_output(schema) 217 self.chain = prompt | structured_llm 108 warned = True 109 emit_warning() --> 110 return wrapped(*args, **kwargs) 199 @beta() 200 def with_structured_output( 201 self, schema: Union[Dict, Type[BaseModel]], **kwargs: Any 202 ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]: 203 """Implement this if there is a way of steering the model to generate responses that match a given schema.""" # noqa: E501 --> 204 raise NotImplementedError() Apparenttly Ollama doesn't output a structured output compatible with LLMGraphTransformer. I couldn't find a way around it.

@@SridharKumarKannam AttributeError on calling LLMGraphTransformer.convert_to_graph_documents

AttributeError on calling LLMGraphTransformer.convert_to_graph_documents having this error it runs ollama server but ends with this error

@@SridharKumarKannam Traceback (most recent call last): File "/home/jelcke/dev/test/txt2graph/openai-graph.py", line 38, in llm_transformer_filtered = LLMGraphTransformer( File "/home/jelcke/dev/test/txt2graph/venv/lib/python3.10/site-packages/langchain_experimental/graph_transformers/llm.py", line 216, in __init__ structured_llm = llm.with_structured_output(schema) File "/home/jelcke/dev/test/txt2graph/venv/lib/python3.10/site-packages/langchain_core/language_models/base.py", line 208, in with_structured_output raise NotImplementedError() NotImplementedError

you can create a .env file and paste your openapi key , OPENAI_API_KEY=your_api_key

You can add key either directly in the code using this command os.environ["OPENAI_API_KEY"] = "YOUR_KEY"

I've setup the key in my environmental variables in the bashrc file

And how can we use aconvert_to_graph_documents function

whats is document size? It is expected to take long time for large documents, for example, if its a pdf book, then all the pages needs to converted to text, a typical book can have 1000s of chunks and for each chunk embedding needs to be create and then stored in the index. Anyway, its mostly a one-off task, for check the end-to-end workflow with short text and then load the entire docs for usage.

with LLMs the results are not always the same, they are stochastic. Set the temperature to very low value. You can add some post-processing to the output of LLMs.

@@SridharKumarKannam I did but still not consistent results 😢

Neo4j

Thanks for your support :)

Yes it is.

many frameworks use openai by default if we don't specify a model explicitly. The key in my config files.

the link is in the description...